10 citations · 40 across the 18 of their papers we have counts for
18 papers
Quantile-based causal inference for spatio-temporal processes: Assessing the impacts of wildfires on US air quality
Zipei Geng, Jordan Richards, Raphael Huser +1
Wildfires pose an increasingly severe threat to air quality, yet quantifying their causal impact remains challenging due to unmeasured meteorological and geographic confounders. Mo…
Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals
Mara Sherlin Talento, Jordan Richards, Raphael Huser +1
We develop a novel characterization of extremal dependence between two cortical regions of the brain when its signals display extremely large amplitudes. We show that connectivity…
Modeling Nonstationary Extremal Dependence via Deep Spatial Deformations
Xuanjie Shao, Jordan Richards, Raphael Huser
Modeling nonstationarity that often prevails in extremal dependence of spatial data can be challenging, and typically requires bespoke or complex spatial models that are difficult…
Separation-based causal discovery for extremes
Junshu Jiang, Jordan Richards, Raphaël Huser +1
Structural causal models (SCMs), with an underlying directed acyclic graph (DAG), provide a powerful analytical framework to describe the interaction mechanisms in large-scale comp…
Spectral Extremal Connectivity of Two-State Seizure Brain Waves
Mara Sherlin D. Talento, Jordan Richards, Marco Pinto-Orellana +2
Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading…
The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency
Junshu Jiang, Jordan Richards, Raphaël Huser +1
In econometrics, the Efficient Market Hypothesis posits that asset prices reflect all available information in the market. Several empirical investigations show that market efficie…
Extreme quantile regression with deep learning
Jordan Richards, Raphaël Huser
Estimation of extreme conditional quantiles is often required for risk assessment of natural hazards in climate and geo-environmental sciences and for quantitative risk management…
Modern extreme value statistics for Utopian extremes
Jordan Richards, Noura Alotaibi, Daniela Cisneros +4
Capturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapol…
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Jordan Richards +1
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where es…
Deep graphical regression for jointly moderate and extreme Australian wildfires
Daniela Cisneros, Jordan Richards, Ashok Dahal +2
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, d…
Neural Bayes estimators for censored inference with peaks-over-threshold models
Jordan Richards, Matthew Sainsbury-Dale, Andrew Zammit-Mangion +1
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances i…
Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning
Jordan Richards, Raphaël Huser, Emanuele Bevacqua +1
Extreme wildfires are a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire ac…
Flexible Modeling of Nonstationary Extremal Dependence using Spatially-Fused LASSO and Ridge Penalties
Xuanjie Shao, Arnab Hazra, Jordan Richards +1
Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Max-stable processes are common choices for modeling spatially-indexed block maxima, w…
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
Jordan Richards, Raphaël Huser
Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events. Useful metrics for quantifying such risk are extreme quantiles…
Joint Estimation of Extreme Spatially Aggregated Precipitation at Different Scales through Mixture Modelling
Jordan Richards, Jonathan A. Tawn, Simon Brown
Although most models for rainfall extremes focus on point-wise values, it is aggregated precipitation over areas up to river catchment scale that is of the most interest. To captur…
On the Tail Behaviour of Aggregated Random Variables
Jordan Richards, Jonathan A. Tawn
In many areas of interest, modern risk assessment requires estimation of the extremal behaviour of sums of random variables. We derive the first order upper-tail behaviour of the w…
Modelling Extremes of Spatial Aggregates of Precipitation using Conditional Methods
Jordan Richards, Jonathan A. Tawn, Simon Brown
Inference on the extremal behaviour of spatial aggregates of precipitation is important for quantifying river flood risk. There are two classes of previous approach, with one faili…
Spatial deformation for non-stationary extremal dependence
Jordan Richards, Jennifer L. Wadsworth
Modelling the extremal dependence structure of spatial data is considerably easier if that structure is stationary. However, for data observed over large or complicated domains, no…